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View all- Gupta SDileep AThenkanidiyoor V(2021)Recognition of varying size scene images using semantic analysis of deep activation mapsMachine Vision and Applications10.1007/s00138-021-01168-832:2Online publication date: 1-Mar-2021
Deep CNN based pseudo-concept selection and modeling for generation of semantic multinomial representation of scene images
Pages 336 - 339
Abstract
Though recent convolutional neural network (CNN) based method for scene classification task show impressive results but lacks in capturing the complex semantic content of the scene images. To reduce the semantic gap a semantic multinomial (SMN) representation is introduced. SMN representation corresponds to a vector of posterior probabilities of concepts. The core part of SMN generation is building the concept model. For building the concept model, it is necessary to have ground truth (true) concept labels for every image in the database. In this research work, we propose novel deep CNN based SMN representation which exploits convolutional layer filters response as pseudo concepts to build the concept model in the absence of true concept labels. The effectiveness of the proposed approach is studied for scene classification tasks on standard datasets like MIT67 and SUN397.
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Published In
January 2018
379 pages
ISBN:9781450363419
DOI:10.1145/3152494
- Conference Chair:
- Sayan Ranu,
- General Chairs:
- Niloy Ganguly,
- Raghu Ramakrishnan,
- Program Chairs:
- Sunita Sarawagi,
- Shourya Roy
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Published: 11 January 2018
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CoDS-COMAD '18
CoDS-COMAD '18: The ACM India Joint International Conference on Data Science & Management of Data
January 11 - 13, 2018
Goa, India
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CODS-COMAD '18 Paper Acceptance Rate 50 of 150 submissions, 33%;
Overall Acceptance Rate 197 of 680 submissions, 29%
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View all- Gupta SDileep AThenkanidiyoor V(2021)Recognition of varying size scene images using semantic analysis of deep activation mapsMachine Vision and Applications10.1007/s00138-021-01168-832:2Online publication date: 1-Mar-2021
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